Remote Sensing Promises Practical Uses in Agriculture Risk Management

Carlos Arce, The World Bank

FARMD (March 2012) | Recent developments in the use of satellite data and its applications for agriculture have intrigued and excited those of us working in the agricultural development arena, particularly with regards to their potential utilization for agricultural risk management.   Expectations for this technology are high, and both academic and scientific practitioners have not come up short in exploring innovative new ways to apply this data in a variety of ways that assist in reducing the risks faced by agriculture stakeholders in developing countries.

The last decade has witnessed an explosion of models, applications, and tools related to the use of satellite observations. The outputs of these tools are being incorporated in Geographical Information Systems (GIS) to analyze agricultural sectors accessible to users.  The importance of these developments becomes more apparent in the context of increasing pressure on agricultural sectors to raise productivity to meet the growing global demand for food.  To meet the rising demand for food it will be necessary for governments and agricultural supply chain participants to make significant investments in agriculture.  For this investment to be forthcoming we will need to see a dramatic improvement in making agricultural risk management more practical.   However, agriculture is a risky business and existing risk models are complex and difficult to understand.  To address this, simpler, accessible and more applicable risk models using remote sensing technologies are needed.  Combining a productivity agenda with improved and more accessible agricultural risk management tools will assist in ensuring the long-term sustainability of agricultural investments, and therefore in agriculture growth itself.

The increasing use of satellite observations to support the design of agricultural risk management strategies promises to provide analytical models and tools with the potential to be applied to the context of developing countries.   At the World Bank, we are closely monitoring the development of these applications and, in recent years, we have seen a wide variety of such analytical tools developed and utilized by practitioners.  The models are now able to diagnose and forecast key processes of intra-annual changes in weather and identify trends in climate change over the longer term.  These applications have sufficient resolution to be used with confidence to inform investment decisions, adopt early warning systems, and for the design of programs for adaptation to climate change.

Although these applications are without a doubt heading in the right direction, and while increasing interest from agricultural stakeholders in developing countries is encouraging, the greatest challenge facing this field is to simplify the complexity of these models to ensure their usability is maximized, and to adapt the analysis to the context of decision making in developing countries.  At the World Bank, we believe it is particularly important to transfer capacity in the design, use and adaptation of applications to different users, to allow for these innovative technologies to be fully exploited in a sustainable manner in developing countries. We need to continue supporting developing countries in adapting these technologies to their needs and in transferring the necessary skills to enable usability and usage to be maximized for all countries and all conditions.  In this way, all those working in this space can contribute to the creation of better conditions for sustainable development increasing agricultural productivity and to reducing poverty.